基于度与路径优先连接的集聚型供应链网络演化模型

集聚型供应链供应链网络具有无标度性、高集聚性等特征.以往研究忽视了供应链网络的高集聚性,使得供应链网络模型不能够准确刻画实际的集聚型供应链网络.本文在具体分析集聚型供应链网络动态演化特征的基础上,提出了基于度与路径优先连接的集聚型供应链网络演化模型,弥补了优先连接仅依赖于节点度值的不足.最后,对集聚型供应链网络的度分布、集聚系数和平均最短路径参数进行了数值模拟,模拟结果表明,该模型不仅能够反映集聚型供应链网络的无标度性,而且能够真实刻画其高集聚性特征.     

城市公共交通复合系统抗毁性仿真研究

运用复杂网络理论,对以成都市为例的城市公共交通复合系统网络以及两子系统网络进行了相关拓扑特性与抗毁性分析。分析结果显示,以成都市为例的地铁-公交复合网络及其子网络均为具有无标度特性的小世界网络,在L、P两种空间中均表现出随机袭击下的鲁棒性与蓄意袭击下的脆弱性,且节点的抗毁性低于边的抗毁性;同时在相同袭击条件下,复合网络的抗毁性均优于地铁子网络与地面公交子网络。     

个体异质对纳什网络存在性的影响研究(英文)

在Bala and Goyal(2000)提出的双向流网络形成模型基础上,研究当个体存在异质性时对纳什网络存在性的影响.分别针对几种不同的环境设定下的个体异质性进行研究,发现个体的连接成本异质性是决定纳什网络存在性的重要因素;但相较于个体的连接成本而言,连接价值的异质性对纳什网络存在性的影响不大.     

基于超图的突发灾害事件演化网络研究

突发灾害事件演化是灾害系统各要素相互作用的结果,从系统要素相互作用的视角剖析突发灾害事件网络演化机理,对阻断次生、衍生事件的发生,减少事件损失,提高应急处置效果具有重要意义.在分析了突发灾害事件网络演化机理基础上,基于超图理论构建了突发灾害事件演化网络模型,通过分析超图的拓扑特性对承灾体的关联性、暴露性以及事件的衍生性、危害性和重要性进行了评估,并基于超图生成的线图对灾害事件的关键演化链进行了描述,最后,以某城市燃气泄露爆炸事件为例进行了验证分析.研究表明,所构建模型既能对突发灾害事件演化网络进行结构化描述,又能对事件及承灾体的风险和重要性进行评估,为分析突发灾害事件网络演化提供了有力工具.     

时延网络控制系统的协同设计方法研究

在搭建的虚拟平台上对多任务的网络控制系统调度和嵌入LQG控制算法进行了仿真.探索了网络控制系统中调度与控制协同设计方法,讨论系统采样周期对网络控制系统的影响.以优化控制系统的性能为目标,以网络的可调度性为条件,结合系统控制和调度算法,对网络控制系统进行静态性能指标估计和动态调度仿真相结合.结果表明该方法既满足了控制系统的性能,又优化了网络的调度,提高了网络的资源率.     

网络借贷平台双边用户交叉网络外部性研究:来自中国P2P网络借贷行业的经验证据

本文基于双边市场理论对网络借贷平台进行模型研究和实证检验,采用格兰杰因果检验方法对典型网络借贷平台样本进行了实证分析,研究结果表明借款人和出借人之间存在交叉网络外部性;且相比出借人而言,借款人是"鸡与蛋"问题的关键点。进而,本文构建了关于网络借贷行业的竞争性双边市场模型。我们发现的证据表明,出借人会寻求高利率的投资标的,借款人会寻求低利率的资金。同时,网络借贷平台上双边用户的数量与用户规模、撮合利率、交易量、借款期限等有关。本文的研究结果意味着,网络借贷平台发展的关键在于拓展借款人市场和平台交易规模,从而提升双边用户的外部性。     

“21世纪海上丝绸之路”沿线港口地位综合评价

本文基于复杂网络节点重要性分析方法,从海运网络分析的视角对“21世纪海上丝绸之路”沿线港口参与“一带一路”建设的地位进行评价。在收集海上丝绸之路沿线102个港口实际数据的基础上,构建海上丝绸之路海运网络拓扑结构图。通过对网络中港口节点的度中心性、接近中心性、中介中心性、特征向量中心性指标进行计算,结合熵权TOPSIS法综合得到港口地位的排序。结果表明新加坡港、上海港、巴生港在网络中具有很高的地位,且现有的复杂网络节点重要性排序方法存在局限,基于熵权TOPSIS法的综合评价得到的港口排序更符合实际发展需求。最后运用节点删除法对排序结果进一步分析,为中国港口参与“一带一路”建设提供参考与建议。     

基于多层次灰色关联分析的创新合作网络节点评估研究

基于网络中心性指标的分析,有效融合网络度中心度、网络中介中心度、网络接近中心度、网络特征向量中心度四种中心性指标的优点,构建多层次灰色关联分析的节点综合评估模型,对网络中节点的重要性进行综合评估,对网络中核心节点予以判定.结合新一代信息技术领域专利合作网络的数据进行分析,结果表明模型在节点重要性排序结果上比以往的方法更科学,不同节点重要性区分度也更高.     

我国股市网络稳定性及其宏观影响因素——基于三种网络拓扑结构、鲁棒性的实证研究

基于金融时间序列的多重分形特征及衡量市场风险的VaR模型,建立我国沪深股市的股票关联网络,实证研究三种网络拓扑结构特征,并使用协整检验方法分析网络稳定性和宏观经济变量间的长期均衡关系。结果表明:股票价格网络不具有无标度性,多标度网络和风险网络都具有无标度性;在三种网络中,风险网络具有更强的鲁棒性。此外,股市波动率和网络稳定系数间互为格兰杰因果关系,股市波动的前期变化能有效解释网络稳定性系数的变化;网络稳定性与宏观经济变量间具有长期的均衡关系,GDP增长率、消费者物价水平CPI对网络稳定性具有正向效应,利率对网络稳定性具有负向效应。风险网络的提出有助于分析我国股市的短期风险及稳定性,并为制定系统风险防御策略提供参考。     

具有偏好锥的链式网络DEA模型

对链式网络DEA模型进行推广,将"偏好锥"引入网络DEA模型.针对中间产出重要性以及决策者评价时的偏好,建立带有产出锥和投入锥相应的两阶段生产可能集,对具有"偏好锥"的链式网络DEA模型,证明了决策单元为网络DEA有效的充要条件,给出了网络DEA有效性与各阶段弱DEA有效性的关系.另外,文章结合具体算例说明了偏好锥的变化对效率评价的影响.关于两阶段的模型以及相关结论可以推广到多阶段网络结构.     

Mapping deontic operators to abductive expectations

Deontic concepts and operators have been widely used in several fields where representation of norms is needed, including legal reasoning and normative multi-agent systems. The EU-funded SOCS project has provided a language to specify the agent interaction in open multi-agent systems. The language is equipped with a declarative semantics based on abductive logic programming, and an operational semantics consisting of a (sound and complete) abductive proof procedure. In the SOCS framework, the specification is used directly as a program for the verification procedure. In this paper, we propose a mapping of the usual deontic operators (obligations, prohibition, permission) to language entities, called expectations, available in the SOCS social framework. Although expectations and deontic operators can be quite different from a philosophical viewpoint, we support our mapping by showing a similarity between the abductive semantics for expectations and the Kripke semantics that can be given to deontic operators. The main purpose of this work is to make the computational machinery from the SOCS social framework available for the specification and verification of systems by means of deontic operators. Marco Alberti received his laurea degree in Electronic Engineering in 2001 and his Ph.D. in Information Engineering in 2005 from the University of Ferrara, Italy. His research interests include constraint logic programming and abductive logic programming, applied in particular to the specification and verification of multi-agent systems. He has been involved as a research assistants in national and European research projects. He currently has a post-doc position in the Department of Engineering at the University of Ferrara. Marco Gavanelli is currently assistant professor in the Department of Engineering at the University of Ferrara, Italy. He graduated in Computer Science Engineering in 1998 at the University of Bologna, Italy. He got his Ph.D. in 2002 at Ferrara University. His research interest include Artificial Intelligence, Constraint Logic Programming, Multi-criteria Optimisation, Abductive Logic Programming, Multi-Agent Systems. He is a member of ALP (the Association for Logic Programming) and AI*IA (the Italian Association for Artificial Intelligence). He has organised workshops, and is author of more than 30 publications between journals and conference proceedings. Evelina Lamma received her degree in Electronic Engineering from University of Bologna, Italy, in 1985 and her Ph.D. degree in Computer Science in 1990. Currently she is Full Professor at the Faculty of Engineering of the University of Ferrara where she teaches Artificial Intelligence and Foundations of Computer Science. Her research activity focuses around: – programming languages (logic languages, modular and object-oriented programming); – artificial intelligence; – knowledge representation; – intelligent agents and multi-agent systems; – machine learning. Her research has covered implementation, application and theoretical aspects. She took part to several national and international research projects. She was responsible of the research group at the Dipartimento di Ingegneria of the University of Ferrara in the UE ITS-2001-32530 Project (named SOCS), in the the context of the UE V Framework Programme - Global Computing Action. Paola Mello received her degree in Electronic Engineering from the University of Bologna, Italy, in 1982, and her Ph.D. degree in Computer Science in 1989. Since 1994 she has been Full Professor. She is enrolled, at present, at the Faculty of Engineering of the University of Bologna (Italy), where she teaches Artificial Intelligence. Her research activity focuses on programming languages, with particular reference to logic languages and their extensions, artificial intelligence, knowledge representation, expert systems with particular emphasis on medical applications, and multi-agent systems. Her research has covered implementation, application and theoretical aspects and is presented in several national and international publications. She took part to several national and international research projects in the context of computational logic. Giovanni Sartor is Marie-Curie professor of Legal informatics and Legal Theory at the European University Institute of Florence and professor of Computer and Law at the University of Bologna (on leave), after obtaining a PhD at the European University Institute (Florence), working at the Court of Justice of the European Union (Luxembourg), being a researcher at the Italian National Council of Research (ITTIG, Florence), and holding the chair in Jurisprudence at Queen’s University of Belfast (where he now is honorary professor). He is co-editor of the Artificial Intelligence and Law Journal and has published widely in legal philosophy, computational logic, legislation technique, and computer law. Paolo Torroni is Assistant Professor in computing at the Faculty of Engineering of the University of Bologna, Italy. He obtained a PhD in Computer Science and Electronic Engineering in 2002, with a dissertation on logic-based agent reasoning and interaction. His research interests mainly focus on computational logic and multi-agent systems research, including logic programming, abductive and hypothetical reasoning, agent interaction, dialogue, negotiation, and argumentation. He is in the steering committee of the CLIMA and DALT international workshops and of the Italian logic programming interest group GULP.     

Scan Statistics on Enron Graphs

We introduce a theory of scan statistics on graphs and apply the ideas to the problem of anomaly detection in a time series of Enron email graphs. Previous presentation: Workshop on Link Analysis, Counterterrorism and Security at the SIAM International Conference on Data Mining, Newport Beach, CA, April 23, 2005. Carey E. Priebe received the B.S. degree in mathematics from Purdue University in 1984, the M.S. degree in computer science from San Diego State University in 1988, and the Ph.D. degree in information technology (computational statistics) from George Mason University in 1993. From 1985 to 1994 he worked as a mathematician and scientist in the US Navy research and development laboratory system. Since 1994 he has been a professor in the Department of Applied Mathematics and Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland. At Johns Hopkins, he holds joint appointments in the Department of Computer Science and the Center for Imaging Science. He is a past President of the Interface Foundation of North America—Computing Science & Statistics, a past Chair of the Section on Statistical Computing of the American Statistical Association, and on the editorial boards of Journal of Computational and Graphical Statistics, Computational Statistics and Data Analysis, and Computational Statistics. His research interests are in computational statistics, kernel and mixture estimates, statistical pattern recognition, statistical image analysis, and statistical inference for high-dimensional and graph data. He was elected Fellow of the American Statistical Association in 2002. John M. Conroy received a B.S. in Mathematics from Saint Joseph's University in 1980 and a Ph.D. in Applied Mathematics from the University of Maryland in 1986. Since then he has been a research staff member for the IDA Center for Computing Sciences in Bowie, MD. His research interest is applications of numerical linear algebra. He is a member of the Society for Industrial and Applied Mathematics, Institute of Electrical and Electronics Engineers (IEEE), and the Association for Computational Linguistics. David J. Marchette received a B.A. in 1980, and an M.A. in mathematics in 1982, from the University of California at San Diego. He received a Ph.D. in Computational Sciences and Informatics in 1996 from George Mason University under the direction of Ed Wegman. From 1985–1994 he worked at the Naval Ocean Systems Center in San Diego doing research on pattern recognition and computational statistics. In 1994 he moved to the Naval Surface Warfare Center in Dahlgren Virginia where he does research in computational statistics and pattern recognition, primarily applied to image processing, text processing, automatic target recognition and computer security. Dr. Marchette is a Fellow of the American Statistical Society. Youngser Park received the B.E. degree in electrical engineering from Inha University in Korea in 1985, the M.S. degree in computer science from The George Washington University in 1991, and had pursued a doctoral degree there. From 1998 to 2000 he worked at the Johns Hopkins Medical Institutes as a senior research engineer. Since 2003 he is working as a research analyst in the Center for Imaging Science at the Johns Hopkins University. His research interests are clustering algorithm, pattern classification, and data mining.     

A 3D modeling of static and forward smoldering combustion in a packed bed of materials

A three-dimensional (3D) model based on the first principles of mass, momentum and energy was developed that numerically simulates the processes of static and forward smoldering in a porous packed bed of plant materials. The packed bed contains cellulose material or tobacco (cigarette) wrapped in a porous paper and surrounded by an ambient air. Other major characteristics of the model are including the effects of buoyancy forces in the flow field, separate treatment of solid and gas in a thermally non-equilibrium environment, and use of multi-precursor kinetic models for the pyrolysis of staring material and oxidation of char. The changes in porosity due to pyrolysis and char oxidation and the effect of porosity on the bed permeability and gas diffusivity are included. The mass, momentum, energy, and species transport equations are solved in a discretized computational domain using a commercially available computational fluid dynamics (CFD) code. The simulation results show that the model reasonably reproduces the major features of a burning cigarette during smoldering and puffing and are in a good agreement with the existing experimental results for cigarettes. Results include the velocity profiles, gas and solid temperatures, coal shape, burn rates, profile and transport of gas and vapor species throughout the packed bed, dilution through the wrapper paper and ventilation in the filter section, and the mass fraction of some pyrolysis and oxidation products in the mainstream and sidestream flows.     

基于知识图谱的运筹学发展现状及趋势研究

借助于Citespace软件对web of science上2006年到2015年之间以运筹学为主题的3166篇学术论文进行分析,梳理了运筹学这十年间的发展脉络、重要文献、研究热点和前沿等。研究发现,目前美国在运筹学研究领域依然遥遥领先,英国、中国等也取得了较为丰硕的成果。近十年对运筹学发展有较大影响的成果主要有Stahlbock R在2008对运筹学在港口的作业组织和控制,码头运作计划和调度应用的总结;19世纪70年代和80年代由Charnes A和Cooper W W以及 Rhodes E开创的CCR和BCC模型;1979年由Garey M R总结的具有三百个具有NP完全性的问题;由Goldberg D E在1989年对于遗传算法的论著等。研究热点领域主要集中在集装箱码头调度运输方面的研究。研究前沿主要集中在管理科学、战略制定、遗传算法、人员排班、数据包络分析等问题。     

强化学习在运筹学的应用:研究进展与展望

强化学习已经成为人工智能领域一个新的研究热点,并已成功应用于各领域,强化学习将运筹优化领域的很多问题视为序贯决策问题,建模为马尔可夫决策过程并进行求解,在求解复杂、动态、随机运筹优化问题具有较大的优势。本文主要对强化学习在运筹优化领域的应用进行综述,首先介绍了强化学习的基本原理及其应用于运筹优化领域的研究框架,然后回顾并总结了强化学习在库存控制、路径优化、装箱配载和车间作业调度等方面的研究成果,并将最新的深度强化学习以及传统方法在运筹学领域的应用研究进行了对比分析,以突出深度强化学习的优越性。最后提出几个值得进一步探讨的研究方向,期望能为强化学习在运筹优化领域的研究提供参考。     

商科型高校理工类研究生创新能力的培养

目前,研究生教育处于我国高等教育的最高层次,肩负着为国家发展培养高技术专业型人才的重要任务.尤其是在各国竞争愈发激烈的今天,理工类研究生更是在未来国家现代化建设、科技创新中发挥着重要作用.但是,由于近几年研究生招生规模的逐渐扩大,研究生的培养模式、培养质量等方面出现了许多亟待解决的问题.从商科型高校理工类研究生的培养现状出发,通过运用文献法和归纳法,本文对当前研究生培养过程中存在的问题进行分析,并针对学生创新能力的培养,从学校、导师和学生自身三方面给出了相应的解决措施,着重强调了数学建模在理工类研究生创新能力培养过程中所发挥的重要作用.最后,以湖南工商大学近几年在理工类研究生创新能力培养实践中所采取的主要举措以及取得的成效来进一步论证本文所提出措施的有效性.     

On the potential of norm-governed behavior in different categories of artificial societies

Based on a classification of artificial societies and the identification of four different types of stakeholders in such societies, we investigate the potential of norm-governed behavior in different types of artificial societies. The basis of the analysis is the preferences of the stakeholders and how they influence the state of the society. A general conclusion drawn is that the more open a society is the more it has to rely on agent owners and designers to achieve norm-governed behavior, whereas in more closed societies the environment designers and owners may control the degree of norm-governed behavior. Paul Davidsson is professor at the Department of Systems and Software Engineering, School of Engineering, Blekinge Institute of Technology, Sweden. He received his Ph.D. in Computer Science in 1996 from Lund University, Sweden. His research interests include the theory and application of multi-agent systems, autonomous agents, and machine learning. Application areas include logistics, transport systems, district heating systems, building automation, and telecommunications systems. The results of this work have been reported in more than 75 peer-reviewed scientific articles published in international journals and conference proceedings. Moreover, he has been the co-editor of three books on Multi Agent Based Simulation and member of program committees of numerous international conferences, such as the International Joint Conference on Autonomous Agents and Multi-Agent Systems Stefan Johansson is an assistant professor at Department of Systems and Software Engineering, Blekinge Institute of Technology, Sweden, where he also finished his PhD in 2002. The main research areas cover coordination issues in multi-agent systems and theories of autonomous agents. Applications of special interests are agents in game ai, robotics, telecommunication networks. On his list of publications are more than 35 peer-reviewed papers published in conference proceedings and scientific journals in the areas of agents, ai, robotics and games. He has also been a member of a variety of programme committees of scientific conferences, including e.g. Intelligent Agent Technology.     

Rawlsian altruism with perfect discrimination leads to social efficiency

Recent studies draw attention on the highly specialized capacity of human beings in recognizing altruists versus cheaters in social interactions. These results hint at the existence of specialized abilities that support discriminating behavior in strategic interactions. In this paper, we explore the implications of discriminating behavior in the study of the indirect evolutionary selection of selfish versus altruistic motivations in the context of generic 2×2 base games, and in particular for coordination and cooperation scenarios. We find that inequality averse (Rawlsian) altruism can enforce under rather general conditions socially optimal outcomes, including cases where selfishness cannot, such as in prisoner’s dilemmas. Inequality seeking (Nietzschian) altruism in no case improves upon Rawlsian altruism in terms of social optimality of outcomes, and often does worse. In the cooperation scenario in particular, Nietzschean altruism never manages to implement the cooperative outcome. Under perfect discrimination, moreover, inequality averse (Rawlsian) altruism often evolves at the expense of selfishness. These results suggest that the development of sophisticated discrimination abilities may be strongly adaptive in supporting fairness-oriented forms of pro-sociality in humans in the context of social dilemmas and coordination problems.     

超网络中心性度量的υ-Position值方法

利用合作博弈理论的分配规则如Shapley值、Banzhaf值等来度量政治、经济和社会网络中节点的中心性或者重要性是识别网络中关键节点的一类重要方法。考虑到在超网络中代表各类组织的超边在网络中发挥的作用不同,本文研究了超网络博弈上一类广义Position值的分配规则,被称为υ-position值。它可以作为网络中度值测度的一类推广,以此来度量网络中参与者的中心性和相对重要性。其次,证明了超网络结构上类Shapley-position值可由分支超边指数和局部平衡超边贡献两个性质所唯一刻画。最后, 举例分析了υ-position值在超网络中心性测度中的应用。     

My agents love to conform: Norms and emotion in the micro-macro link

This contribution investigates the function of emotion in relation to norms, both in natural and artificial societies. We illustrate that unintentional behavior can be normative and socially functional at the same time, thereby highlighting the role of emotion. Conceiving of norms as mental objects we then examine the role of emotion in maintaining and enforcing such propositional attitudes. The findings are subsequently related to social structural dynamics and questions concerning micro-macro linkage, in natural societies as well as in artificial systems. Finally, we outline the possibilities of an application to the socionic multi-agent architecture SONAR. Christian von Scheve graduated in Sociology with minors in Psychology, Economics, and Political Science at the University of Hamburg, where he also worked as a research assistant at the Institute of Sociology. Currently, he is a 3rd year PhD student at the University of Hamburg. He was a Fellow of the Research Group “Emotions as Bio-Cultural Processes” at the Center for Interdisciplinary Research (ZiF) at Bielefeld University. In his doctoral thesis he develops an interdisciplinary approach to emotion and social structural dynamics, integrating emotion theories from the neurosciences, psychology, and the social sciences. He has published on the role of emotion in large-scale social systems, human-computer interaction, and multi-agent systems. He is co-editor of a forthcoming volume on emotion regulation. Daniel Moldt received his BSc in Computer Science/Software Engineering from the University of Birmingham (England) in 1984, graduated in Informatics at the University of Hamburg, with a minor in Economics in 1990. He received his PhD in Informatics from the University of Hamburg in 1996, where he has been a researcher and lecturer at the Department of Informatics since 1990. Daniel Moldt is also the head of the Laboratory for Agent-Oriented Systems (LAOS) of the theoretical foundations group at the Department of Informatics. His research interests focus on theoretical foundations, software engineering and distributed systems with an emphasis on agent technology, Petri nets, specification languages, intra- and inter-organizational application development, Socionics and emotion in informatics. Julia Fix is currently a PhD student at the Theoretical Foundations of Computer Science Group, Department for Informatics at the University of Hamburg. She studied Informatics and Psychology at the University of Hamburg, with an emphasis on theoretical foundations of multi-agent systems and wrote her diploma theses about emotional agent systems. Her current research interests focus on conceptual challenges and theoretical foundations of modelling emotions in multi-agent systems, emotion-based norm enforcement and maintenance, and Socionics. A further research focus are Petri nets, in particular the use of Petri-net modelling formalisms for representing different aspects of emotion in agent systems. Rolf von Lüde is a professor of Sociology at the University of Hamburg with a focus in teaching and research in Sociology of Organizations, Work and Industry since 1996. He graduated in Economics, Sociology, and Psychology, and received his doctorate in Economics and the venia legendi in Sociology from the University of Dortmund. His current research focuses on labor conditions, the organization of production, social change and the educational system, the organizational structures of university, Socionics as a new approach to distributed artificial intelligence in cooperation with computer scientists, new public management, and emotions and social structures. Rolf von Lüde is currently Head of Department of Social Sciences and Vice Dean of the School of Business, Economics and Social Sciences, University of Hamburg.